## Developer Guide and Reference

• 2021.6
• 04/11/2022
• Public Content
Contents

# Basic Statistics

Basic statistics algorithm computes the following set of quantitative dataset characteristics:
• minimums/maximums
• sums
• means
• sums of squares
• sums of squared differences from the means
• second order raw moments
• variances
• standard deviations
• variations
 Operation Computational methods Programming Interface

## Programming Interface

All types and functions in this section are declared in the
oneapi::dal::basic_statistics
namespace and are available via inclusion of the
oneapi/dal/algo/basic_statistics.hpp
Descriptor
template<typename
Float
= detail::descriptor_base<>::float_t, typename
Method
= detail::descriptor_base<>::method_t, typename
class
descriptor
Template Parameters
• Float
– The floating-point type that the algorithm uses for intermediate computations. Can be
float
or
double
.
• Method
– Tag-type that specifies an implementation of algorithm. Can be
method::dense
.
– Tag-type that specifies the type of the problem to solve. Can be
.
Properties
result_option_id
result_options
Choose which results should be computed and returned.
Getter & Setter

result_option_id get_result_options() const
auto & set_result_options(const result_option_id &value)

Method tags
struct
dense
Tag-type that denotes dense computational method.
using
by_default
= dense
Alias tag-type for dense computational method.
struct
compute
Tag-type that parameterizes entities that are used to compute statistics.
using
by_default
= compute
Alias tag-type for the compute task.
Training
compute(...)
Input
template<typename
class
compute_input
Template Parameters
– Tag-type that specifies the type of the problem to solve. Can be
.
Constructors
compute_input
(
const
table &
data
)
Creates a new instance of the class with the given
data
property value.
Properties
const
table &
data
An table with the training data, where each row stores one feature vector.
Default value
: table{}.
Getter & Setter

const table & get_data() const
auto & set_data(const table &data)

Result
template<typename
class
compute_result
Template Parameters
– Tag-type that specifies the type of the problem to solve. Can be
.
Constructors
compute_result
()
Creates a new instance of the class with the default property values.
Properties
const
table &
variation
A table, where element is the variation result for feature .
Default value
: table{}.
Getter & Setter

const table & get_variation() const
auto & set_variation(const table &value)

const
table &
mean
A table, where element is the mean result for feature .
Default value
: table{}.
Getter & Setter

const table & get_mean() const
auto & set_mean(const table &value)

const
table &
sum_squares_centered
A table, where element is the sum_squares_centered result for feature .
Default value
: table{}.
Getter & Setter

const table & get_sum_squares_centered() const
auto & set_sum_squares_centered(const table &value)

const
table &
second_order_raw_moment
A table, where element is the second_order_raw_moment result for feature .
Default value
: table{}.
Getter & Setter

const table & get_second_order_raw_moment() const
auto & set_second_order_raw_moment(const table &value)

const
table &
variance
A table, where element is the variance result for feature .
Default value
: table{}.
Getter & Setter

const table & get_variance() const
auto & set_variance(const table &value)

const
table &
sum_squares
A table, where element is the sum_squares result for feature .
Default value
: table{}.
Getter & Setter

const table & get_sum_squares() const
auto & set_sum_squares(const table &value)

const
result_option_id &
result_options
Result options that indicates availability of the properties.
Default value
: full set of.
Getter & Setter

const result_option_id & get_result_options() const
auto & set_result_options(const result_option_id &value)

const
table &
sum
A table, where element is the sum result for feature .
Default value
: table{}.
Getter & Setter

const table & get_sum() const
auto & set_sum(const table &value)

const
table &
max
A table, where element is the maximum result for feature .
Default value
: table{}.
Getter & Setter

const table & get_max() const
auto & set_max(const table &value)

const
table &
standard_deviation
A table, where element is the standard_deviation result for feature .
Default value
: table{}.
Getter & Setter

const table & get_standard_deviation() const
auto & set_standard_deviation(const table &value)

const
table &
min
A table, where element is the minimum result for feature .
Default value
: table{}.
Getter & Setter

const table & get_min() const
auto & set_min(const table &value)

Operation
template<typename
Descriptor
> basic_statistics::compute_result
compute
(
const
Descriptor &
desc
,
const
basic_statistics::compute_input &
input
)
Parameters
• desc
– Basic statistics algorithm descriptor
basic_statistics::descriptor
• input
– Input data for the computing operation
Preconditions

input.data.is_empty == false

#### Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.